FLCIR

hht（希尔伯特黄变换）的matlab工具箱，其中包括时频分析工具箱(hht (Hilbert-Huang Transform) of the matlab toolbox, including the time-frequency analysis toolbox)

MATLAB实现单神经元PID控制，在智能控制算法中，运行结果较好。(MATLAB single-neuron PID control, intelligent control algorithm, the results better.)

this is the selection diversity technique, used for avoiding the effects of fading.

说明：  era函数，求频率，阵型，阻尼比,用于模态分析的数据处理，等等(era,frequency,damping ratio)

这个程序是MATLAB的程序，它能够实现中心频率是4000Hz的带通滤波器的功能，在4000Hz的信号上面加了噪声信号，通过滤波器滤除杂波(This program is MATLAB program that enables the center frequency is 4000Hz band-pass filter function in the 4000Hz signal above the noise signal added, filtered through the clutter filter)

This PDF introduce some vital information about electrical instrument in Persian Language.

关于matlab编译器的一些教学源代码，共四个代码(About the matlab compiler some teaching source code, a total of four code)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

Using the nearest neighbor algorithm for clustering and classification of random samples and calculate the classification rate, compared to the algorithm including modified versions

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